Least-Squares Support Vector Machine for Regression Model with Crisp Inputs-Gaussian Fuzzy Output

  • Hwang, Chang-Ha (Dept. of Statistical Information, Catholic University of Daegu)
  • Published : 2004.05.31

Abstract

Least-squares support vector machine (LS-SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose LS-SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.